Job Description
Are you ready to define the future? Nexus Horizon Labs is on the cutting edge of technological evolution, and we are seeking a visionary Senior AI Architect to spearhead the 2026 Horizon Initiative. In this pivotal role, you will architect the neural frameworks and safety protocols that will define our relationship with next-generation artificial intelligence. We are not just building software; we are engineering the ethical and functional bedrock for the year 2026 and beyond.
As a leader in this niche, you will work in a high-performance environment where the boundary between theoretical AI and practical application is constantly expanding. We offer competitive compensation, equity packages, and the opportunity to work on projects that will change the world.
Responsibilities
- Lead Architecture Design: Design and implement scalable, robust neural network architectures tailored for the 2026 computing paradigm, including hybrid quantum-classical systems.
- AI Alignment & Safety: Develop and enforce rigorous safety protocols to ensure AI alignment with human values, preventing hallucinations and ensuring ethical decision-making by 2026.
- Model Optimization: Optimize deep learning models for high-speed inference on next-gen hardware, pushing the boundaries of processing speed and energy efficiency.
- Cross-Functional Leadership: Collaborate with quantum researchers, ethicists, and software engineers to bridge the gap between theoretical AI and real-world application.
- Research & Innovation: Stay ahead of the curve in emerging AI trends (AGI, Synthetic Data, Embodied AI) and integrate them into our core product suite.
- Mentorship: Mentor a team of junior researchers and data scientists, fostering a culture of continuous learning and technical excellence.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Mathematics, Physics, or a related field with a focus on Artificial Intelligence.
- Experience: Minimum of 7+ years of experience in machine learning engineering, deep learning, or AI research roles.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and experience with distributed computing frameworks (e.g., Kubernetes, Ray).
- Domain Knowledge: Deep understanding of Large Language Models (LLMs), Natural Language Processing (NLP), and reinforcement learning.
- Problem Solving: Demonstrated ability to solve complex, ambiguous problems and think critically about the long-term implications of AI systems.
- Soft Skills: Exceptional communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.